Applications of Deep Learning in the Real World

What Can Neural Networks Do?

Deep learning, a subclass of machine learning based on the ideas and principles of artificial neural networks, has enormous potential and has already found wide application in various industries. This is a modern and powerful tool that is able to solve many problems that we simply did not have answers to before.

Deep learning has many real-world applications across industries. Here are several classes of problems solved by neural networks and specific examples:

• Image Recognition: Deep learning is applied to image recognition and classification in many areas, from automatic tagging of social media photos to medical diagnosis, such as analyzing MRI images or X-ray images;

• Speech recognition: Systems like Siri, Google Assistant, and Alexa use deep learning to process and understand human speech;

• Text analysis: Deep learning helps in the analysis and classification of texts. This includes customer reviews, news articles, social media and more. An example would be sentiment analysis in tweets or product reviews;

• Recommender Systems: Services like Netflix or Amazon use deep learning to offer personalized recommendations based on previous user behavior;
• Self-driving cars: Deep learning allows cars to recognize objects, pedestrians, other vehicles, road signs, and more, and subsequently make decisions based on the information received;

• Facial Recognition: This is used in many areas, from phone unlocking to security systems and keyless entry systems;

• Generative tasks: These are used to create new data that mimics some of the original data. Examples include creating realistic images of faces that do not exist in reality, or transforming an image of a winter landscape into a summer one. This also applies to tasks related to text and audio processing.

What Can NOT Neural Networks Do?

At the same time, there are some classes of problems that are currently difficult or impossible to solve using deep learning or neural networks:

• Building Artificial General Intelligence (AGI): Despite all the advances in deep learning, we are still far from building a machine that can fully simulate human intelligence in all its diversity. Each neural network is capable of solving only a pre-learned task;

• Data-poor tasks: Deep learning requires large amounts of data for training. If there is little data, the model may learn poorly (underfitting) or remember the data without extracting the necessary patterns (overfitting);

• High requirements for interpretability: Neural networks are often called "black boxes" because it is difficult to understand how they came to a certain conclusion or prediction. For some areas, such as medicine or finance, where a high degree of transparency and explainability is required, this can be a problem;

• Tasks that require strict adherence to rules: Neural networks are good at learning from data and predicting based on patterns found in the data, but they may not be able to cope with tasks where strict rules or algorithms must be strictly followed (e.g. solving the equation).

In general, deep learning is a powerful tool that can solve many problems. However, like any tool, it has its limitations and it is important to use it where it makes the most sense.